Dynamical Coupling for Cardiopulmonary-Cerebral Resuscitation Using Recurrence-Based Analyses

Cardiopulmonary resuscitation (CPR), as the emergency treatment of cardiac arrest (CA) patients, its quality is directly linked to patient survival rates and the recovery of neurological function. However, the physiological monitoring indicators widely used in current clinical practice often exist in isolation, thus making it difficult to comprehensively reveal the complex interaction mechanisms among the heart, lungs, and brain during CPR. In light of this, the study introduced nonlinear dynamical coupling analysis techniques, aiming to explore the coupling dynamic changes and potential mechanisms of the cardiopulmonary-cerebral system during CPR. Twelve Bama pigs were selected as experimental subjects, and CA models were successfully established in them, followed by standardized CPR treatment. During the experimental process, electrocardiogram (ECG), electroencephalogram (EEG), and end-tidal carbon dioxide (ETCO2) signals were collected. Recurrence quantification analysis was employed to calculate nonlinear dynamical parameters recurrence rate (RR), determinism (DET), and longest diagonal length (Lmax) for ECG, EEG, and ETCO and Joint recurrence quantification analysis (JRQA) was employed for their coupling. JRQA results showed that DET and Lmax during CA were significantly higher than all other stages (p < 0.01), and RR during CA were significantly higher than during CPR (p < 0.01) and recovery ofspontaneous circulation (p < 0.05). The results indicate that monitoring the cardiopulmonary-cerebral system during CA and CPR exhibits higher sensitivity, enabling a more accurate reflection of the dynamic changes in the patient's physiological state.